From 75178865fc959fcb7d114333ece4a625e456a17a Mon Sep 17 00:00:00 2001 From: TrafficCop <103712563+TrafficCop@users.noreply.github.com> Date: Mon, 15 Jan 2024 16:17:34 -0800 Subject: [PATCH] Revert "Revert "Merge pull request #135 from cheejung/main"" This reverts commit f3c69a23735aa9115c597504b942f5e5c8f9014f. --- CONTRIBUTORS.md | 1 + gitignore | 3 + poetry.lock | 1618 +++++++++++++++++++++- pyproject.toml | 1 + tests/devices/biostrap/test_evo.py | 91 ++ wearipedia/__init__.py | 1 + wearipedia/devices/__init__.py | 1 + wearipedia/devices/biostrap/__init__.py | 1 + wearipedia/devices/biostrap/evo.py | 169 +++ wearipedia/devices/biostrap/evo_fetch.py | 219 +++ wearipedia/devices/biostrap/evo_gen.py | 291 ++++ 11 files changed, 2395 insertions(+), 1 deletion(-) create mode 100644 gitignore create mode 100644 tests/devices/biostrap/test_evo.py create mode 100644 wearipedia/devices/biostrap/__init__.py create mode 100644 wearipedia/devices/biostrap/evo.py create mode 100644 wearipedia/devices/biostrap/evo_fetch.py create mode 100644 wearipedia/devices/biostrap/evo_gen.py diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index ad667f56..abeb0a75 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -9,3 +9,4 @@ - Jack Hung (jjhung66@stanford.edu) - Saarth Shah (saarth@berkeley.edu) - Suvan Kumar (kumarsuvan0@gmail.com) +- Hee Jung Choi (cheejung@stanford.edu) diff --git a/gitignore b/gitignore new file mode 100644 index 00000000..937ab4f7 --- /dev/null +++ b/gitignore @@ -0,0 +1,3 @@ +.DS_Store +.project +*.pyc diff --git a/poetry.lock b/poetry.lock index 46607eb4..5b644e26 100644 --- a/poetry.lock +++ b/poetry.lock @@ -22,6 +22,108 @@ files = [ {file = "ansicon-1.89.0.tar.gz", hash = "sha256:e4d039def5768a47e4afec8e89e83ec3ae5a26bf00ad851f914d1240b444d2b1"}, ] +[[package]] +name = "anyio" +version = "3.7.1" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false +python-versions = ">=3.7" +files 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"sha256:9ec291ba87c2dfad42c3d5b6f68713fa18be1acd7476569516b2431682315c17"}, +] + [[package]] name = "wrapt" version = "1.16.0" @@ -2392,4 +4008,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.8, <4.0" -content-hash = "edd413aa1ea4db1439d2eb1a8c142078bf123f2284b4d6bd6632400f5febfa66" +content-hash = "5ca660dd9696a5fa1795efda5eee550e3cd552deb6a9934d84ef70af502d7eee" diff --git a/pyproject.toml b/pyproject.toml index 53d9ff1d..0f1c2434 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -46,6 +46,7 @@ fbm = "^0.3.0" beautifulsoup4 = "^4.12.2" myfitnesspal = "^2.0.1" polyline = "^2.0.0" +jupyter = "^1.0.0" [tool.poetry.group.dev.dependencies] bandit = "^1.7.1" diff --git a/tests/devices/biostrap/test_evo.py b/tests/devices/biostrap/test_evo.py new file mode 100644 index 00000000..478a9b5c --- /dev/null +++ b/tests/devices/biostrap/test_evo.py @@ -0,0 +1,91 @@ +from datetime import datetime, timedelta + +import pytest + +import wearipedia + +data_formats = { + "bpm": (int, float), + "brpm": (int, float), + "hrv": (int, float), + "spo2": (int, float), + "rest_cals": (int, float), + "work_cals": (int, float), + "active_cals": (int, float), + "step_cals": (int, float), + "total_cals": (int, float), + "steps": (int, float), + "distance": (int, float), +} + + +@pytest.mark.parametrize("real", [True, False]) +def test_evo(real): + start_synthetic = datetime(2023, 6, 5) + end_synthetic = datetime(2023, 6, 20, 23, 59, 59) + + device = wearipedia.get_device( + "biostrap/evo", + start_date=datetime.strftime(start_synthetic, "%Y-%m-%d"), + end_date=datetime.strftime(end_synthetic, "%Y-%m-%d"), + ) + if real: + wearipedia._authenticate_device("evo", device) + + for data_type, data_format in data_formats.items(): + data = device.get_data(data_type) + + # Checks specific to date-keyed data + if data_type in [ + "rest_cals", + "work_cals", + "active_cals", + "step_cals", + "total_cals", + ]: + dates = list(data.keys()) + + # Check dates are consecutive and within the range + expected_date = start_synthetic + for date_str in dates: + date = datetime.strptime(date_str, "%Y-%m-%d") + assert ( + date == expected_date + ), f"Expected date {expected_date}, but got {date}" + expected_date += timedelta(days=1) + + # Checks specific to datetime-keyed data + elif data_type in ["steps", "distance"]: + + datetimes = [ + datetime.strptime(dt_str, "%Y-%m-%d %H:%M:%S") for dt_str in data.keys() + ] + + # Check that datetimes are within the specified range. + for dt in datetimes: + assert ( + start_synthetic <= dt <= end_synthetic + ), f"Datetime {dt} out of range" + + # Check that the datetimes are sequential and increase by a minute. + datetimes.sort() + for i in range(1, len(datetimes)): + expected_dt = datetimes[i - 1] + timedelta(minutes=1) + assert ( + datetimes[i] == expected_dt + ), f"Expected datetime {expected_dt}, but got {datetimes[i]}" + else: + datetimes = [key[0] for key in data.keys()] + + # Check datetimes are within the range + for datetime_str in datetimes: + dt = datetime.strptime(datetime_str, "%Y-%m-%d %H:%M:%S") + assert ( + start_synthetic <= dt <= end_synthetic + ), f"Datetime {dt} out of range" + + # Check data values are of expected type + for value in data.values(): + assert isinstance( + value, data_format + ), f"{data_type} data {value} is not a {data_format.__name__}" diff --git a/wearipedia/__init__.py b/wearipedia/__init__.py index 7829048b..d0dcab3f 100644 --- a/wearipedia/__init__.py +++ b/wearipedia/__init__.py @@ -61,6 +61,7 @@ def get_all_device_names(): return [ "apple/healthkit", + "biostrap/evo", "cronometer/cronometer", "whoop/whoop_4", "withings/scanwatch", diff --git a/wearipedia/devices/__init__.py b/wearipedia/devices/__init__.py index 407770e9..de5bf674 100644 --- a/wearipedia/devices/__init__.py +++ b/wearipedia/devices/__init__.py @@ -1,4 +1,5 @@ from .apple import * +from .biostrap import * from .cronometer import * from .dexcom import * from .dreem import * diff --git a/wearipedia/devices/biostrap/__init__.py b/wearipedia/devices/biostrap/__init__.py new file mode 100644 index 00000000..7b42cdc0 --- /dev/null +++ b/wearipedia/devices/biostrap/__init__.py @@ -0,0 +1 @@ +from .evo import * diff --git a/wearipedia/devices/biostrap/evo.py b/wearipedia/devices/biostrap/evo.py new file mode 100644 index 00000000..09cd44ff --- /dev/null +++ b/wearipedia/devices/biostrap/evo.py @@ -0,0 +1,169 @@ +import os +import pickle +import webbrowser +from datetime import datetime +from urllib.parse import urlencode + +import requests + +from ...utils import seed_everything +from ..device import BaseDevice +from .evo_fetch import * +from .evo_gen import * + +class_name = "EVO" + + +class EVO(BaseDevice): + def __init__(self, seed=0, start_date="2023-06-05", end_date="2023-06-20"): + params = { + "seed": seed, + "start_date": str(start_date), + "end_date": str(end_date), + } + + self._initialize_device_params( + [ + "activities", + "bpm", + "brpm", + "hrv", + "spo2", + "rest_cals", + "work_cals", + "active_cals", + "step_cals", + "total_cals", + "sleep_session", + "sleep_detail", + "steps", + "distance", + ], + params, + { + "seed": 0, + "synthetic_start_date": "2023-06-05", + "synthetic_end_date": "2023-06-20", + }, + ) + + def _default_params(self): + return { + "start_date": self.init_params["synthetic_start_date"], + "end_date": self.init_params["synthetic_end_date"], + } + + def _get_real(self, data_type, params): + return fetch_real_data( + self.access_token, params["start_date"], params["end_date"], data_type + ) + + def _filter_synthetic(self, data, data_type, params): + start_date = params["start_date"] + end_date = params["end_date"] + + start_datetime = f"{start_date} 00:00:00" + end_datetime = f"{end_date} 23:59:59" + + # For data types that are stored with a date string as a key + if data_type in [ + "rest_cals", + "work_cals", + "active_cals", + "step_cals", + "total_cals", + ]: + return { + date: value + for date, value in data.items() + if start_date <= date <= end_date + } + + # For data types that are stored with a datetime string as a key in a tuple + elif data_type in [ + "bpm", + "brpm", + "spo2", + "hrv", + ]: + return { + key: value + for key, value in data.items() + if start_datetime <= key[0] <= end_datetime + } + + # For steps and distance that use datetime strings as keys (assuming distance also has datetime as per steps) + elif data_type in ["steps", "distance"]: + return { + datetime: value + for datetime, value in data.items() + if start_datetime <= datetime <= end_datetime + } + + else: + return data + + def _gen_synthetic(self): + # generate random data according to seed + seed_everything(self.init_params["seed"]) + # and based on start and end dates + ( + self.activities, + self.bpm, + self.brpm, + self.hrv, + self.spo2, + self.rest_cals, + self.work_cals, + self.active_cals, + self.step_cals, + self.total_cals, + self.sleep_session, + self.sleep_detail, + self.steps, + self.distance, + ) = create_syn_data( + self.init_params["synthetic_start_date"], + self.init_params["synthetic_end_date"], + ) + + # We get the access token to make requests to the Biostrap API + def _authenticate(self, auth_creds): + self.client_id = auth_creds["client_id"] + self.client_secret = auth_creds["client_secret"] + redirect_uri = "https://127.0.0.1:8080" + token_url = "https://auth.biostrap.com/token" + + # Generate the authorization URL and open it in the default web browser + params = { + "client_id": self.client_id, + "response_type": "code", + "redirect_uri": redirect_uri, + } + authorization_url = f"https://auth.biostrap.com/authorize?{urlencode(params)}" + + # Instruct the user to open the URL in their browser () + print( + f"Please go to the following URL and authorize access: {authorization_url}" + ) + print( + "If a page cannot be displayed, please make sure you are logged into the Biostrap account in your browser." + ) + + # Get the authorization response URL from the command line (the method Jack and I talked about didn't work) + authorization_response = input("Enter the full callback URL: ") + + code = authorization_response.split("code=")[1].split("&")[0] + + # Now we can request the access token + data = { + "grant_type": "authorization_code", + "code": code, + "redirect_uri": redirect_uri, + } + auth = (self.client_id, self.client_secret) + response = requests.post(token_url, auth=auth, data=data) + + # Get the access token from the response + token_json = response.json() + self.access_token = token_json["access_token"] diff --git a/wearipedia/devices/biostrap/evo_fetch.py b/wearipedia/devices/biostrap/evo_fetch.py new file mode 100644 index 00000000..a4a537dd --- /dev/null +++ b/wearipedia/devices/biostrap/evo_fetch.py @@ -0,0 +1,219 @@ +import json +from datetime import datetime, timedelta + +import requests + + +def fetch_real_data(access_token, start_date, end_date, data_type): + """ + Fetch specified data from the Biostrap API within a given date range. + + This function can retrieve a variety of health-related metrics, such as activities, + heart rate variability, steps, sleep data, and more. It adapts to different endpoints + based on the type of data requested and iteratively fetches data to ensure comprehensive retrieval. + + :param access_token: The access token to authenticate requests to the Biostrap API. + :type access_token: str + :param start_date: The start date of the range for which data should be retrieved, in "YYYY-MM-DD" format. + :type start_date: str + :param end_date: The end date of the range for which data should be retrieved, in "YYYY-MM-DD" format. + :type end_date: str + :param data_type: The category of data to fetch. Must be one of the following: + "activities", "bpm", "brpm", "hrv", "spo2", "rest_cals", "work_cals", "active_cals", + "step_cals", "total_cals", "sleep_session", "sleep_detail", "steps", "distance". + :type data_type: str + + :return: A dictionary containing the retrieved data. The format varies based on the data_type. + :rtype: Dict + + :raises ValueError: If an unsupported data_type is provided. + :raises Exception: If the API request encounters errors. + """ + print(f"Fetching {data_type} data from Biostrap API...") + print(f"Start date: {start_date}, End date: {end_date}") + + BASE_URL = "https://api-beta.biostrap.com/v1/" + headers = {"Authorization": f"Bearer {access_token}"} + + # Convert start and end dates to datetime objects for comparison + start_date_obj = datetime.strptime(start_date, "%Y-%m-%d") + end_date_obj = datetime.strptime(end_date, "%Y-%m-%d") + + all_data = {} + + if data_type in [ + "activities", + "bpm", + "brpm", + "hrv", + "spo2", + "steps", + "distance", + ]: + + if data_type == "activities": + ENDPOINT = "activities" + elif data_type in ["steps", "distance"]: + ENDPOINT = "steps" + else: + ENDPOINT = "biometrics" + + URL = f"{BASE_URL}{ENDPOINT}" + + last_timestamp = 0 + limit = 50 # The max limit allowed by API + + while True: + params = {"last-timestamp": last_timestamp, "limit": limit} + response = requests.get(URL, params=params, headers=headers) + + if response.status_code != 200: + raise Exception( + f"Request failed with status code {response.status_code}" + ) + + data = response.json() + + if data_type in ["activities", "steps", "distance"]: + for activity in data["data"]: + + timestamp = activity["timestamp"] / 1000 # Convert to seconds + current_datetime = datetime.fromtimestamp(timestamp) + + # Break the loop if the timestamp is beyond the end date + if current_datetime > end_date_obj: + break + + # Include data only if the timestamp is within the given date range + if start_date_obj <= current_datetime <= end_date_obj: + if data_type == "activities": + all_data[ + current_datetime.strftime("%Y-%m-%d %H:%M:%S") + ] = activity + elif data_type == "steps": + all_data[ + current_datetime.strftime("%Y-%m-%d %H:%M:%S") + ] = activity["steps"] + else: + all_data[ + current_datetime.strftime("%Y-%m-%d %H:%M:%S") + ] = activity["distance"] + else: + for biometric in data["data"]: + timestamp = biometric["timestamp"] / 1000 # Convert to seconds + current_datetime = datetime.fromtimestamp(timestamp) + + if current_datetime > end_date_obj: + break + + if start_date_obj <= current_datetime <= end_date_obj: + key = ( + current_datetime.strftime("%Y-%m-%d %H:%M:%S"), + biometric["tz_offset_mins"], + ) + all_data[key] = biometric[data_type] + + if "next" in data["links"]: + last_timestamp = data["data"][-1]["timestamp"] + else: + break + + return all_data + + elif data_type in [ + "rest_cals", + "work_cals", + "active_cals", + "step_cals", + "total_cals", + ]: + + metric_mapping = { + "rest_cals": "Resting Calories", + "work_cals": "Workout Calories", + "active_cals": "Active Calories", + "step_cals": "Step Calories", + "total_cals": "Total Calories", + } + + URL = f"{BASE_URL}calorie/details" + + while start_date_obj <= end_date_obj: + + end_date_for_first_query = start_date_obj + timedelta(days=29) + if end_date_for_first_query > end_date_obj: + end_date_for_first_query = end_date_obj + + response = requests.get( + URL, + params={ + "date": end_date_for_first_query.strftime("%Y-%m-%d"), + "granularity": "month", + }, + headers=headers, + ) + + if response.status_code != 200: + raise Exception( + f"Request failed with status code {response.status_code}" + ) + + data = response.json() + + for metric in data["metrics"]: + if metric["type"] == metric_mapping[data_type]: + for timeseries_data in metric["timeseries"]: + date_str = timeseries_data["date"] + if ( + start_date_obj + <= datetime.strptime(date_str, "%Y-%m-%d") + <= end_date_for_first_query + ): + all_data[date_str] = timeseries_data["value"] + break + start_date_obj = end_date_for_first_query + timedelta(days=1) + + return all_data + + elif data_type in ["sleep_session", "sleep_detail"]: + + if data_type == "sleep_session": + ENDPOINT = "sleep" + else: + ENDPOINT = "sleep/details/day" + + URL = f"{BASE_URL}{ENDPOINT}" + + # We'll loop through each date between the start_date_obj and end_date_obj (inclusive) + current_date = start_date_obj + while current_date <= end_date_obj: + # For each date, we'll fetch the relevant data + params = {"date": current_date.strftime("%Y-%m-%d")} + response = requests.get(URL, params=params, headers=headers) + + # If empty response, move to the next date + if response.status_code == 204: + current_date += timedelta(days=1) + continue + + data = response.json() + + # Check for successful response + if response.status_code != 200: + raise Exception( + f"Request failed for date {current_date.strftime('%Y-%m-%d')} with status code {response.status_code}" + ) + + # Check if the data exists in the response + if data_type == "sleep_session" and "data" in data and data["data"]: + all_data[current_date.strftime("%Y-%m-%d")] = data["data"] + elif data_type == "sleep_detail" and data: + all_data[current_date.strftime("%Y-%m-%d")] = data + + # Move to the next date + current_date += timedelta(days=1) + + return all_data + + else: + raise ValueError(f"Invalid data_type: {data_type}") diff --git a/wearipedia/devices/biostrap/evo_gen.py b/wearipedia/devices/biostrap/evo_gen.py new file mode 100644 index 00000000..6dd359c3 --- /dev/null +++ b/wearipedia/devices/biostrap/evo_gen.py @@ -0,0 +1,291 @@ +import math +import random +from datetime import datetime, timedelta + +import numpy as np + + +def create_syn_data(start_date, end_date): + """ + Generates synthetic data collected by Biostrap between a given start and end date. + + :param start_date: Start date (inclusive) as a string in the format "YYYY-MM-DD" + :type start_date: str + :param end_date: End date (inclusive) as a string in the format "YYYY-MM-DD" + :type end_date: str + + :return: A tuple consisting of: + - activities: Dictionary containing details of a random synthetic activity + - bpm: Dictionary representing beats per minute for every 10 seconds throughout the range + - brpm: Dictionary representing breaths per minute based on bpm values for every minute throughout the range + - hrv: Dictionary representing heart rate variability for every 10 seconds throughout the range + - spo2: Dictionary representing blood oxygen saturation for every 10 seconds + - rest_cals: Dictionary representing resting calories burned each day + - work_cals: Dictionary representing workout calories burned each day + - active_cals: Dictionary representing active calories burned each day + - step_cals: Dictionary representing calories burned from steps each day + - total_cals: Dictionary representing total calories burned each day + - sleep_session: Dictionary representing moments of movement during typical sleeping hours + - sleep_detail: Dictionary representing details of a synthetic sleep session + - steps: Dictionary representing steps taken every minute throughout the range + - distance: Dictionary representing distance covered (based on steps) every minute throughout the range + + :rtype: Tuple[Dict, Dict, Dict, Dict, Dict, Dict, Dict, Dict, Dict, Dict, Dict, Dict, Dict, Dict] + """ + + # Convert the provided strings to datetime objects + start_date_obj = datetime.strptime(start_date, "%Y-%m-%d") + end_date_obj = datetime.strptime(end_date, "%Y-%m-%d") + + TZ_OFFSET = -420 + + # Adjusted Gaussian noise functions for each biometric with more realistic standard deviations + def gaussian_noise_bpm(): + return np.random.normal(0, 2) # Reduced standard deviation for less noise + + def gaussian_noise_brpm(): + return np.random.normal(0, 0.5) # Reduced standard deviation for less noise + + def gaussian_noise_hrv(): + return np.random.normal(0, 1) # Reduced standard deviation for less noise + + def gaussian_noise_spo2(): + return np.random.normal(0, 0.05) # Reduced standard deviation for minimal noise + + def synthetic_biometrics(start_date_obj, end_date_obj): + bpm = {} + brpm = {} + hrv = {} + spo2 = {} + + curr_date_obj = start_date_obj + time_index = 0 + + # Initial values for random walk + bpm_current = 45 + brpm_current = 16 + hrv_current = 40 + spo2_current = 98 + + # Target means and bounds + target_mean_bpm = 70 + target_mean_brpm = 16 + target_mean_hrv = 50 + target_mean_spo2 = 98 + + bpm_lower_bound, bpm_upper_bound = 40, 120 + brpm_lower_bound, brpm_upper_bound = 12, 20 + hrv_lower_bound, hrv_upper_bound = 20, 100 + spo2_lower_bound, spo2_upper_bound = 95, 100 + + # Store the last minute's average bpm to calculate brpm + last_minute_bpm = [] + + while curr_date_obj <= end_date_obj: + curr_time = curr_date_obj + while curr_time < curr_date_obj + timedelta(days=1): + key = (curr_time.strftime("%Y-%m-%d %H:%M:%S"), TZ_OFFSET) + + # Update bpm, hrv, and spo2 every 10 seconds + bpm_mean_reversion = ( + target_mean_bpm - bpm_current + ) * 0.01 # Changed from 0.1 + bpm_current += bpm_mean_reversion + gaussian_noise_bpm() + bpm_current = max(bpm_lower_bound, min(bpm_upper_bound, bpm_current)) + bpm[key] = int(bpm_current) + + hrv_mean_reversion = (target_mean_hrv - hrv_current) * 0.1 + hrv_current += hrv_mean_reversion + gaussian_noise_hrv() + hrv_current = max(hrv_lower_bound, min(hrv_upper_bound, hrv_current)) + hrv[key] = int(hrv_current) + + spo2_mean_reversion = (target_mean_spo2 - spo2_current) * 0.1 + spo2_current += spo2_mean_reversion + gaussian_noise_spo2() + spo2_current = max( + spo2_lower_bound, min(spo2_upper_bound, spo2_current) + ) + spo2[key] = int(spo2_current) + + # Update brpm every minute + if time_index % 60 == 0: + brpm_mean_reversion = (target_mean_brpm - brpm_current) * 0.1 + brpm_current += brpm_mean_reversion + gaussian_noise_brpm() + brpm_current = max( + brpm_lower_bound, min(brpm_upper_bound, brpm_current) + ) + brpm[key] = int(brpm_current) + + # Increment time and index + curr_time += timedelta(seconds=10) + time_index += 10 + + curr_date_obj += timedelta(days=1) + + return bpm, brpm, hrv, spo2 + + def synthetic_steps_distance_per_minute(bpm_dict): + steps_dict = {} + distance_dict = {} + + # For each minute, we'll check the BPM to determine steps + for key, bpm_value in bpm_dict.items(): + dt, _ = key + + # Extracting hour to check for sleeping hours + curr_hour = int(dt.split(" ")[1].split(":")[0]) + + if dt.endswith("00"): # Checking if it's on a per-minute mark + if 23 <= curr_hour or curr_hour < 6: # typical sleeping hours + steps = random.choice( + [0, 0, 0, 0, 1, 2] + ) # Mostly zero, but sometimes a small number indicating tossing/turning in sleep + elif bpm_value < 60: + steps = random.randint(0, 20) # Relatively calm/resting + elif bpm_value < 80: + steps = random.randint(20, 40) # Maybe just light walking + else: + steps = random.randint(40, 120) # Active movement or jogging + + distance = steps * random.uniform(0.7, 0.8) + date_str = dt.split()[0] + time_str = dt.split()[1] + + steps_dict[f"{date_str} {time_str}"] = steps + distance_dict[f"{date_str} {time_str}"] = distance + + return steps_dict, distance_dict + + def synthetic_daily_calories(bpm_dict, steps_dict): + rest_cals_dict = {} + work_cals_dict = {} + active_cals_dict = {} + step_cals_dict = {} + total_cals_dict = {} + + daily_bpm_averages = {} + + # Precompute average BPM per day + for (dt, _), v in bpm_dict.items(): + date_str = dt.split(" ")[0] + daily_bpm_averages.setdefault(date_str, []).append(v) + + # Compute average BPM for each day + for date_str, bpm_vals in daily_bpm_averages.items(): + avg_bpm = sum(bpm_vals) / len(bpm_vals) + daily_bpm_averages[date_str] = avg_bpm + + # Calculate calories based on the precomputed average BPMs + for date_str, avg_bpm in daily_bpm_averages.items(): + if avg_bpm < 60: + active_cals = random.randint(50, 100) # relatively inactive + elif avg_bpm < 80: + active_cals = random.randint(100, 200) # moderately active + else: + active_cals = random.randint(200, 300) # very active + + steps_val = sum( + [val for dt, val in steps_dict.items() if dt.startswith(date_str)] + ) + + rest_cals_dict[date_str] = random.randint(1000, 1300) + work_cals_dict[date_str] = random.randint(300, 600) + step_cals_dict[date_str] = steps_val * 0.05 + active_cals_dict[date_str] = active_cals + total_cals_dict[date_str] = ( + rest_cals_dict[date_str] + + work_cals_dict[date_str] + + step_cals_dict[date_str] + + active_cals_dict[date_str] + ) + + return ( + rest_cals_dict, + work_cals_dict, + active_cals_dict, + step_cals_dict, + total_cals_dict, + ) + + def synthetic_activity(): + # A sample workout + activity_date = (start_date_obj + (end_date_obj - start_date_obj) / 2).strftime( + "%Y-%m-%d" + ) # choose a day in the middle of the range + + return { + "activity_date": activity_date, + "type": "Running", + "duration": timedelta(minutes=random.randint(20, 60)), + "distance": random.uniform(3, 10), + "calories_burned": random.randint(200, 500), + "avg_bpm": random.randint(80, 150), + "peak_bpm": random.randint(150, 180), + "steps_taken": random.randint(3000, 10000), + "intensity": random.choice(["light", "moderate", "high"]), + } + + def synthetic_sleep_session(bpm_dict): + # Extracting nighttime bpm readings + night_movements = { + k: v + for k, v in bpm_dict.items() + if 23 <= int(k[0].split(" ")[1].split(":")[0]) + or int(k[0].split(" ")[1].split(":")[0]) < 6 + } + # Filter out readings where bpm indicates deep sleep (low bpm) + night_movements = {k: v for k, v in night_movements.items() if v > 65} + return night_movements + + def synthetic_sleep_detail(): + sleep_date = (start_date_obj + (end_date_obj - start_date_obj) / 2).strftime( + "%Y-%m-%d" + ) + total_sleep_duration = 8 # Assuming 8 hours sleep + + light_sleep = random.uniform(0.4, 0.6) * total_sleep_duration + deep_sleep = random.uniform(0.2, 0.3) * total_sleep_duration + rem_sleep = total_sleep_duration - (light_sleep + deep_sleep) + + return { + "sleep_date": sleep_date, + "light_sleep": light_sleep, + "deep_sleep": deep_sleep, + "rem_sleep": rem_sleep, + "awake_time": random.uniform(0.1, 0.3), + "times_awoken": random.randint(1, 5), + } + + # Generate biometric, steps, and distance data + bpm, brpm, hrv, spo2 = synthetic_biometrics(start_date_obj, end_date_obj) + steps, distance = synthetic_steps_distance_per_minute(bpm) + + # Generate daily calories based on steps and bpm + rest_cals, work_cals, active_cals, step_cals, total_cals = synthetic_daily_calories( + bpm, steps + ) + + # Generate activity data + activities = synthetic_activity() + + # Generate sleep session data + sleep_session = synthetic_sleep_session(bpm) + + # Generate sleep detail data + sleep_detail = synthetic_sleep_detail() + + return ( + activities, + bpm, + brpm, + hrv, + spo2, + rest_cals, + work_cals, + active_cals, + step_cals, + total_cals, + sleep_session, + sleep_detail, + steps, + distance, + )